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DBMS > Databricks vs. Hive vs. SiteWhere

System Properties Comparison Databricks vs. Hive vs. SiteWhere

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonHive  Xexclude from comparisonSiteWhere  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.data warehouse software for querying and managing large distributed datasets, built on HadoopM2M integration platform for persisting/querying time series data
Primary database modelDocument store
Relational DBMS
Relational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.06
Rank#356  Overall
#35  Time Series DBMS
Websitewww.databricks.comhive.apache.orggithub.com/­sitewhere/­sitewhere
Technical documentationdocs.databricks.comcwiki.apache.org/­confluence/­display/­Hive/­Homesitewhere1.sitewhere.io/­index.html
DeveloperDatabricksApache Software Foundation infoinitially developed by FacebookSiteWhere
Initial release201320122010
Current release3.1.3, April 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoCommon Public Attribution License Version 1.0
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava
Server operating systemshostedAll OS with a Java VMLinux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yespredefined scheme
Typing infopredefined data types such as float or dateyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.yesno
Secondary indexesyesyesno
SQL infoSupport of SQLwith Databricks SQLSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
HTTP REST
Supported programming languagesPython
R
Scala
C++
Java
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes infouser defined functions and integration of map-reduce
Triggersno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesyesselectable replication factorselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users, groups and rolesUsers with fine-grained authorization concept
More information provided by the system vendor
DatabricksHiveSiteWhere
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksHiveSiteWhere
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21 May 2024, Fortune

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